diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index 6c0258e..b302ef4 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -662,7 +662,7 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
             }
         }
 
-        let n_trees = self.trees.as_ref().unwrap().len() as f64;
+        let n_trees: f64 = self.trees.as_ref().unwrap().len() as f64;
         probas.mul_scalar_mut(1.0 / n_trees);
 
         Ok(probas)
@@ -884,22 +884,22 @@ mod tests {
         // These values are approximate and based on typical random forest behavior
         for i in 0..(pro_n_rows / 2) {
             assert!(
-                *probas.get((i, 0)) > f64::from_f32(0.6).unwrap(),
+                f64::from_f32(0.6).unwrap().lt(probas.get((i, 0))),
                 "Class 0 samples should have high probability for class 0"
             );
             assert!(
-                *probas.get((i, 1)) < f64::from_f32(0.4).unwrap(),
+                f64::from_f32(0.4).unwrap().gt(probas.get((i, 1))),
                 "Class 0 samples should have low probability for class 1"
             );
         }
 
         for i in (pro_n_rows / 2)..pro_n_rows {
             assert!(
-                *probas.get((i, 1)) > f64::from_f32(0.6).unwrap(),
+                f64::from_f32(0.6).unwrap().lt(probas.get((i, 1))),
                 "Class 1 samples should have high probability for class 1"
             );
             assert!(
-                *probas.get((i, 0)) < f64::from_f32(0.4).unwrap(),
+                f64::from_f32(0.4).unwrap().gt(probas.get((i, 0))),
                 "Class 1 samples should have low probability for class 0"
             );
         }